{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import json\n",
    "\n",
    "feed = {}\n",
    "feed[\"000002.SZ\"] = pd.read_csv(\"000002.SZ.csv\", parse_dates=[\"trade_date\"], index_col=\"trade_date\")\n",
    "order_lst = json.load(open(\"order_hist.json\"))\n",
    "for order in order_lst:\n",
    "    for deal in order[\"deal_lst\"]:\n",
    "        if order[\"type\"] == \"buy\":\n",
    "            order[\"date\"] = pd.to_datetime(deal[\"date\"])\n",
    "            break\n",
    "        deal[\"close_date\"] = pd.to_datetime(deal[\"close_date\"])\n",
    "        deal[\"open_date\"] =pd.to_datetime(deal[\"open_date\"])\n",
    "\n",
    "class Stat:\n",
    "    pass\n",
    "stat = Stat()\n",
    "stat.data = pd.read_csv(\"stat.csv\", parse_dates=[\"date\"], index_col=\"date\")\n",
    "stat.total_returns = 0.23147\n",
    "stat.annual_return = 0.22442\n",
    "stat.max_dropdown = 0.02792\n",
    "stat.sharpe = -0.000547\n",
    "\n",
    "net_profit = stat.data.assets_value[-1] - stat.data.assets_value[0]\n",
    "profit_lst = []\n",
    "hold_time_lst = []\n",
    "commission = 0\n",
    "for order in order_lst:\n",
    "    for deal in order[\"deal_lst\"]:\n",
    "        commission += deal[\"commission\"]\n",
    "        if order[\"type\"] == \"buy\":\n",
    "            break\n",
    "        profit_lst.append(deal[\"profit\"])\n",
    "        close_date = deal[\"close_date\"]\n",
    "        open_date = deal[\"open_date\"]\n",
    "        time_diff = close_date - open_date\n",
    "        # 3600 * 24\n",
    "        hold_time = time_diff.total_seconds() / 86400\n",
    "        hold_time_lst.append(hold_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "# @Author: youerning\n",
    "# @Email: 673125641@qq.com\n",
    "\n",
    "\"\"\"\n",
    "构图规划\n",
    "\n",
    "总收益, 年化收益, 最大回撤, 夏普比率, 手续费比率(手续费/收益)\n",
    "收益曲线\n",
    "-------\n",
    "持仓时间直方分布图|持仓收益直方分布图\n",
    "-------\n",
    "持有过的股票走势图，并且标记买入点/卖出点\n",
    "\"\"\"\n",
    "import matplotlib.pyplot as plt\n",
    "# import matplotlib as mpl\n",
    "\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['simhei']  # 指定默认字体\n",
    "plt.rcParams['axes.unicode_minus'] = False   # 解决保存图像是负号'-'显示为方块的问题\n",
    "\n",
    "\n",
    "class Plotter(object):\n",
    "    def __init__(self, feed, stat, order_lst):\n",
    "        self.feed = feed\n",
    "        self.stat = stat\n",
    "        self.order_lst = order_lst\n",
    "\n",
    "    def report(self, file_name):\n",
    "        fig = self.plot()\n",
    "        fig.savefig(file_name)\n",
    "\n",
    "    def plot(self):\n",
    "        net_profit = self.stat.data.assets_value[-1] - self.stat.data.assets_value[0]\n",
    "        profit_lst = []\n",
    "        hold_time_lst = []\n",
    "        commission = 0\n",
    "        for order in self.order_lst:\n",
    "            for deal in order[\"deal_lst\"]:\n",
    "                commission += deal[\"commission\"]\n",
    "                if order[\"type\"] == \"buy\":\n",
    "                    break\n",
    "                profit_lst.append(deal[\"profit\"])\n",
    "                close_date = deal[\"close_date\"]\n",
    "                open_date = deal[\"open_date\"]\n",
    "                time_diff = close_date - open_date\n",
    "                # 3600 * 24\n",
    "                hold_time = time_diff.total_seconds() / 86400\n",
    "                hold_time_lst.append(hold_time)\n",
    "\n",
    "        title = \"策略收益: {:.3f}% 年化收益: {:.3f}% 最大回撤: {:.3f}% 夏普比率: {:.3f}% 手续费比率: {:.3f}%\\n\\n策略走势\"\n",
    "        title = title.format(self.stat.total_returns * 100,\n",
    "                             self.stat.annual_return * 100,\n",
    "                             self.stat.max_dropdown * 100,\n",
    "                             self.stat.sharpe,\n",
    "                             (commission / net_profit) * 100)\n",
    "        fig = plt.figure(figsize=(12, 16))\n",
    "        fig.subplots_adjust(wspace=0.3, hspace=0.3)\n",
    "        code_lst = {order[\"code\"] for order in self.order_lst}\n",
    "        rows = len(code_lst) + 2\n",
    "#         date_formatter = mpl.dates.DateFormatter(\"%Y-%m-%d\")\n",
    "\n",
    "        # 绘制收益走势图\n",
    "        ax_trend = fig.add_subplot(rows, 1, 1)\n",
    "        ax_trend.set_title(title)\n",
    "        self.stat.data.assets_value.plot(ax=ax_trend, sharex=False)\n",
    "        ax_trend.set_xlabel(\"\")\n",
    "#         ax_trend.plot_date(self.stat.index, self.stat.assets_value)\n",
    "#         ax_trend.xaxis.set_major_fomatter(date_formatter)\n",
    "\n",
    "        # 绘制直方图\n",
    "        ax_hist1 = fig.add_subplot(rows, 3, 4)\n",
    "        ax_hist2 = fig.add_subplot(rows, 3, 5)\n",
    "        ax_hist3 = fig.add_subplot(rows, 3, 6)\n",
    "\n",
    "        if len(profit_lst) > 0:\n",
    "            ax_hist1.hist(profit_lst)\n",
    "        if len(hold_time_lst) > 0:\n",
    "            ax_hist3.hist(hold_time_lst)\n",
    "        if len(hold_time_lst) > 0 and len(profit_lst) > 0:\n",
    "            ax_hist2.hist2d(profit_lst, hold_time_lst)\n",
    "\n",
    "        ax_hist1.set_title(\"收益分布\")\n",
    "        ax_hist2.set_title(\"收益分布 vs 持仓时间分布\")\n",
    "        ax_hist3.set_title(\"持仓时间分布\")\n",
    "\n",
    "        # 绘制持仓股票走势图\n",
    "        for idx, code in enumerate(code_lst, start=rows):\n",
    "            ax = fig.add_subplot(rows, 1, idx)\n",
    "            hist = self.feed[code]\n",
    "            hist.close.plot(ax=ax, sharex=False)\n",
    "            for order in self.order_lst:\n",
    "                if len(order[\"deal_lst\"]) == 0:\n",
    "                    continue\n",
    "                if order[\"type\"] == \"buy\":\n",
    "                    for deal in order[\"deal_lst\"]:\n",
    "                        ax.annotate(\"\",\n",
    "                                    xy=(deal[\"date\"], deal[\"price\"]),\n",
    "                                    xytext=(deal[\"date\"], deal[\"price\"] * 0.95),\n",
    "                                    arrowprops=dict(facecolor=\"r\",\n",
    "                                                    alpha=0.3,\n",
    "                                                    headlength=10,\n",
    "                                                    width=10))\n",
    "\n",
    "                if order[\"type\"] == \"sell\":\n",
    "                    for deal in order[\"deal_lst\"]:\n",
    "                        ax.annotate(\"\",\n",
    "                                    xy=(deal[\"close_date\"], deal[\"close_price\"]),\n",
    "                                    xytext=(deal[\"close_date\"], deal[\"close_price\"] * 1.05),\n",
    "                                    arrowprops=dict(facecolor=\"g\",\n",
    "                                                    alpha=0.3,\n",
    "                                                    headlength=10,\n",
    "                                                    width=10))\n",
    "        return fig\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1152 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotter = Plotter(feed, stat, order_lst)\n",
    "plotter.report(\"report.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"b7c6041b-9e7d-4109-9056-a98ade0eef40\" data-root-id=\"1793\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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\",\"dtype\":\"float64\",\"shape\":[1688]}},\"selected\":{\"id\":\"1882\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1883\",\"type\":\"UnionRenderers\"}},\"id\":\"1827\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"months\":[0,4,8]},\"id\":\"1879\",\"type\":\"MonthsTicker\"},{\"attributes\":{},\"id\":\"1819\",\"type\":\"HelpTool\"},{\"attributes\":{\"callback\":null},\"id\":\"1798\",\"type\":\"DataRange1d\"},{\"attributes\":{\"months\":[0,6]},\"id\":\"1880\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"text\":\"\\u7b56\\u7565\\u6536\\u76ca\"},\"id\":\"1794\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"1881\",\"type\":\"YearsTicker\"},{\"attributes\":{\"callback\":null},\"id\":\"1796\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1882\",\"type\":\"Selection\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1829\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"1800\",\"type\":\"LinearScale\"},{\"attributes\":{\"data_source\":{\"id\":\"1827\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1828\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1829\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"1831\",\"type\":\"CDSView\"}},\"id\":\"1830\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"1883\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"1802\",\"type\":\"LinearScale\"},{\"attributes\":{\"source\":{\"id\":\"1827\",\"type\":\"ColumnDataSource\"}},\"id\":\"1831\",\"type\":\"CDSView\"},{\"attributes\":{\"formatter\":{\"id\":\"1868\",\"type\":\"DatetimeTickFormatter\"},\"ticker\":{\"id\":\"1805\",\"type\":\"DatetimeTicker\"}},\"id\":\"1804\",\"type\":\"DatetimeAxis\"},{\"attributes\":{},\"id\":\"1866\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"num_minor_ticks\":5,\"tickers\":[{\"id\":\"1870\",\"type\":\"AdaptiveTicker\"},{\"id\":\"1871\",\"type\":\"AdaptiveTicker\"},{\"id\":\"1872\",\"type\":\"AdaptiveTicker\"},{\"id\":\"1873\",\"type\":\"DaysTicker\"},{\"id\":\"1874\",\"type\":\"DaysTicker\"},{\"id\":\"1875\",\"type\":\"DaysTicker\"},{\"id\":\"1876\",\"type\":\"DaysTicker\"},{\"id\":\"1877\",\"type\":\"MonthsTicker\"},{\"id\":\"1878\",\"type\":\"MonthsTicker\"},{\"id\":\"1879\",\"type\":\"MonthsTicker\"},{\"id\":\"1880\",\"type\":\"MonthsTicker\"},{\"id\":\"1881\",\"type\":\"YearsTicker\"}]},\"id\":\"1805\",\"type\":\"DatetimeTicker\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"1869\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"ticker\":{\"id\":\"1805\",\"type\":\"DatetimeTicker\"}},\"id\":\"1808\",\"type\":\"Grid\"},{\"attributes\":{\"mantissas\":[1,2,5],\"max_interval\":500.0,\"num_minor_ticks\":0},\"id\":\"1870\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{\"formatter\":{\"id\":\"1866\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1810\",\"type\":\"BasicTicker\"}},\"id\":\"1809\",\"type\":\"LinearAxis\"}],\"root_ids\":[\"1793\"]},\"title\":\"Bokeh Application\",\"version\":\"1.3.1\"}};\n",
       "  var render_items = [{\"docid\":\"af78f234-8322-4376-bfa6-8eaccae2763c\",\"roots\":{\"1793\":\"b7c6041b-9e7d-4109-9056-a98ade0eef40\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1793"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.plotting import figure, output_notebook, show\n",
    "\n",
    "\n",
    "p = figure(title=\"策略收益\", plot_width=800, plot_height=250, x_axis_type=\"datetime\")\n",
    "\n",
    "p.line(stat.data.index, stat.data.assets_value, color='navy', alpha=0.5)\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"0f070481-1a8a-412b-bc98-385587a646af\" data-root-id=\"2508\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = {\"4b4728d1-7620-4e6d-a19d-b9f6f77533d1\":{\"roots\":{\"references\":[{\"attributes\":{\"children\":[{\"id\":\"2507\",\"type\":\"ToolbarBox\"},{\"id\":\"2505\",\"type\":\"GridBox\"}]},\"id\":\"2508\",\"type\":\"Column\"},{\"attributes\":{},\"id\":\"2435\",\"type\":\"HelpTool\"},{\"attributes\":{},\"id\":\"2469\",\"type\":\"HelpTool\"},{\"attributes\":{},\"id\":\"2418\",\"type\":\"LinearScale\"},{\"attributes\":{\"callback\":null},\"id\":\"2448\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"2503\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"tools\":[{\"id\":\"2430\",\"type\":\"PanTool\"},{\"id\":\"2431\",\"type\":\"WheelZoomTool\"},{\"id\":\"2432\",\"type\":\"BoxZoomTool\"},{\"id\":\"2433\",\"type\":\"SaveTool\"},{\"id\":\"2434\",\"type\":\"ResetTool\"},{\"id\":\"2435\",\"type\":\"HelpTool\"},{\"id\":\"2464\",\"type\":\"PanTool\"},{\"id\":\"2465\",\"type\":\"WheelZoomTool\"},{\"id\":\"2466\",\"type\":\"BoxZoomTool\"},{\"id\":\"2467\",\"type\":\"SaveTool\"},{\"id\":\"2468\",\"type\":\"ResetTool\"},{\"id\":\"2469\",\"type\":\"HelpTool\"}]},\"id\":\"2506\",\"type\":\"ProxyToolbar\"},{\"attributes\":{},\"id\":\"2416\",\"type\":\"LinearScale\"},{\"attributes\":{\"data_source\":{\"id\":\"2477\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"2478\",\"type\":\"Quad\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"2479\",\"type\":\"Quad\"},\"selection_glyph\":null,\"view\":{\"id\":\"2481\",\"type\":\"CDSView\"}},\"id\":\"2480\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"source\":{\"id\":\"2477\",\"type\":\"ColumnDataSource\"}},\"id\":\"2481\",\"type\":\"CDSView\"},{\"attributes\":{\"toolbar\":{\"id\":\"2506\",\"type\":\"ProxyToolbar\"},\"toolbar_location\":\"above\"},\"id\":\"2507\",\"type\":\"ToolbarBox\"},{\"attributes\":{\"text\":\"\\u6536\\u76ca\\u5206\\u5e03\"},\"id\":\"2410\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"2421\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"2450\",\"type\":\"LinearScale\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"2430\",\"type\":\"PanTool\"},{\"id\":\"2431\",\"type\":\"WheelZoomTool\"},{\"id\":\"2432\",\"type\":\"BoxZoomTool\"},{\"id\":\"2433\",\"type\":\"SaveTool\"},{\"id\":\"2434\",\"type\":\"ResetTool\"},{\"id\":\"2435\",\"type\":\"HelpTool\"}]},\"id\":\"2436\",\"type\":\"Toolbar\"},{\"attributes\":{\"callback\":null},\"id\":\"2412\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"2452\",\"type\":\"LinearScale\"},{\"attributes\":{\"bottom\":{\"value\":0},\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"navy\"},\"left\":{\"field\":\"left\"},\"line_alpha\":{\"value\":0.5},\"line_color\":{\"value\":\"white\"},\"right\":{\"field\":\"right\"},\"top\":{\"field\":\"top\"}},\"id\":\"2483\",\"type\":\"Quad\"},{\"attributes\":{\"formatter\":{\"id\":\"2500\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"2455\",\"type\":\"BasicTicker\"}},\"id\":\"2454\",\"type\":\"LinearAxis\"},{\"attributes\":{\"below\":[{\"id\":\"2454\",\"type\":\"LinearAxis\"}],\"center\":[{\"id\":\"2458\",\"type\":\"Grid\"},{\"id\":\"2463\",\"type\":\"Grid\"}],\"left\":[{\"id\":\"2459\",\"type\":\"LinearAxis\"}],\"plot_height\":300,\"plot_width\":400,\"renderers\":[{\"id\":\"2485\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"2444\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"2470\",\"type\":\"Toolbar\"},\"toolbar_location\":null,\"x_range\":{\"id\":\"2446\",\"type\":\"DataRange1d\"},\"x_scale\":{\"id\":\"2450\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"2448\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"2452\",\"type\":\"LinearScale\"}},\"id\":\"2443\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"bottom\":{\"value\":0},\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"left\":{\"field\":\"left\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"right\":{\"field\":\"right\"},\"top\":{\"field\":\"top\"}},\"id\":\"2484\",\"type\":\"Quad\"},{\"attributes\":{},\"id\":\"2455\",\"type\":\"BasicTicker\"},{\"attributes\":{\"below\":[{\"id\":\"2420\",\"type\":\"LinearAxis\"}],\"center\":[{\"id\":\"2424\",\"type\":\"Grid\"},{\"id\":\"2429\",\"type\":\"Grid\"}],\"left\":[{\"id\":\"2425\",\"type\":\"LinearAxis\"}],\"plot_height\":300,\"plot_width\":400,\"renderers\":[{\"id\":\"2480\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"2410\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"2436\",\"type\":\"Toolbar\"},\"toolbar_location\":null,\"x_range\":{\"id\":\"2412\",\"type\":\"DataRange1d\"},\"x_scale\":{\"id\":\"2416\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"2414\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"2418\",\"type\":\"LinearScale\"}},\"id\":\"2409\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"data_source\":{\"id\":\"2482\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"2483\",\"type\":\"Quad\"},\"hover_glyph\":null,\"m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Application\",\"version\":\"1.3.1\"}};\n",
       "  var render_items = [{\"docid\":\"4b4728d1-7620-4e6d-a19d-b9f6f77533d1\",\"roots\":{\"2508\":\"0f070481-1a8a-412b-bc98-385587a646af\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "2508"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.plotting import gridplot\n",
    "import numpy as np\n",
    "\n",
    "p1 = figure(title=\"收益分布\", plot_width=400, plot_height=300)\n",
    "p2 = figure(title=\"持仓时间分布\", plot_width=400, plot_height=300)\n",
    "\n",
    "hist1, edges1 = np.histogram(profit_lst, bins=30)\n",
    "hist2, edges2 = np.histogram(hold_time_lst, bins=30)\n",
    "\n",
    "p1.quad(top=hist1, bottom=0, left=edges1[:-1], right=edges1[1:],\n",
    "       fill_color=\"navy\", line_color=\"white\", alpha=0.5)\n",
    "\n",
    "p2.quad(top=hist2, bottom=0, left=edges2[:-1], right=edges2[1:],\n",
    "       fill_color=\"navy\", line_color=\"white\", alpha=0.5)\n",
    "\n",
    "show(gridplot([[p1, p2]]))\n",
    "# show(p1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "unexpected attribute 'palette' to HexTile, possible attributes are aspect_scale, fill_alpha, fill_color, js_event_callbacks, js_property_callbacks, line_alpha, line_cap, line_color, line_dash, line_dash_offset, line_join, line_width, name, orientation, q, r, scale, size, subscribed_events or tags",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-72-762a0356c300>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvisible\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 20\u001b[1;33m \u001b[0mr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbins\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mp2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhex_tile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhover_color\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"pink\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhover_alpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.8\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Spectral11'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     21\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     22\u001b[0m \u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mfakesource\u001b[0m in \u001b[0;36mhextile\u001b[1;34m(self, q, r, **kwargs)\u001b[0m\n",
      "\u001b[1;32mc:\\users\\ye\\python\\python36\\lib\\site-packages\\bokeh\\plotting\\helpers.py\u001b[0m in \u001b[0;36mfunc\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m    855\u001b[0m             \u001b[0mmglyph_ca\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    856\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 857\u001b[1;33m         \u001b[0mglyph\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_make_glyph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mglyphclass\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mglyph_ca\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    858\u001b[0m         \u001b[0mnsglyph\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_make_glyph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mglyphclass\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnsglyph_ca\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    859\u001b[0m         \u001b[0msglyph\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_make_glyph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mglyphclass\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msglyph_ca\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\ye\\python\\python36\\lib\\site-packages\\bokeh\\plotting\\helpers.py\u001b[0m in \u001b[0;36m_make_glyph\u001b[1;34m(glyphclass, kws, extra)\u001b[0m\n\u001b[0;32m    396\u001b[0m     \u001b[0mkws\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkws\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    397\u001b[0m     \u001b[0mkws\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mextra\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 398\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mglyphclass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkws\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    399\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    400\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\ye\\python\\python36\\lib\\site-packages\\bokeh\\model.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m    305\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"id\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    306\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 307\u001b[1;33m         \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mModel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    308\u001b[0m         \u001b[0mdefault_theme\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_to_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    309\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\ye\\python\\python36\\lib\\site-packages\\bokeh\\core\\has_props.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, **properties)\u001b[0m\n\u001b[0;32m    251\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    252\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mproperties\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 253\u001b[1;33m             \u001b[0msetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    254\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\ye\\python\\python36\\lib\\site-packages\\bokeh\\core\\has_props.py\u001b[0m in \u001b[0;36m__setattr__\u001b[1;34m(self, name, value)\u001b[0m\n\u001b[0;32m    286\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    287\u001b[0m             raise AttributeError(\"unexpected attribute '%s' to %s, %s attributes are %s\" %\n\u001b[1;32m--> 288\u001b[1;33m                 (name, self.__class__.__name__, text, nice_join(matches)))\n\u001b[0m\u001b[0;32m    289\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    290\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__str__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: unexpected attribute 'palette' to HexTile, possible attributes are aspect_scale, fill_alpha, fill_color, js_event_callbacks, js_property_callbacks, line_alpha, line_cap, line_color, line_dash, line_dash_offset, line_join, line_width, name, orientation, q, r, scale, size, subscribed_events or tags"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from bokeh.plotting import figure, show\n",
    "from bokeh.layouts import row\n",
    "\n",
    "# normal distribution center at x=0 and y=5\n",
    "x = np.array(profit_lst)\n",
    "y = np.array(hold_time_lst)\n",
    "\n",
    "H, xe, ye = np.histogram2d(x, y, bins=10)\n",
    "\n",
    "# produce an image of the 2d histogram\n",
    "p = figure(x_range=(min(xe), max(xe)), y_range=(min(ye), max(ye)), title='Image')\n",
    "\n",
    "p.image(image=[H], x=xe[0], y=ye[0], dw=xe[-1] - xe[0], dh=ye[-1] - ye[0], palette=\"Spectral11\")\n",
    "\n",
    "# produce hexbin plot\n",
    "p2 = figure(title=\"Hexbin\", match_aspect=True)\n",
    "p.grid.visible = False\n",
    "\n",
    "r, bins = p2.hex_tile(x, y, size=1, hover_color=\"pink\", hover_alpha=0.8, palette='Spectral11')\n",
    "\n",
    "show(row(p, p2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
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      "application/vnd.bokehjs_exec.v0+json": ""
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       "id": "16019"
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     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "from bokeh.plotting import figure, show\n",
    "from bokeh.layouts import row\n",
    "\n",
    "# normal distribution center at x=0 and y=5\n",
    "x = np.array(profit_lst)\n",
    "y = np.array(hold_time_lst)\n",
    "\n",
    "H, xe, ye = np.histogram2d(x, y, bins=30)\n",
    "\n",
    "# produce an image of the 2d histogram\n",
    "p = figure(x_range=(min(xe), max(xe)), y_range=(min(ye), max(ye)), title='Image')\n",
    "\n",
    "p.image(image=[H], x=xe[0], y=ye[0], dw=xe[-1] - xe[0], dh=ye[-1] - ye[0], palette=\"Spectral11\")\n",
    "\n",
    "# produce hexbin plot\n",
    "# p2 = figure(title=\"Hexbin\", match_aspect=True)\n",
    "# p.grid.visible = False\n",
    "\n",
    "# r, bins = p2.hexbin(x, y, size=100, hover_color=\"pink\", hover_alpha=0.8, palette='Spectral11')\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"31089\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"31089\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };var element = document.getElementById(\"31089\");\n",
       "  if (element == null) {\n",
       "    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '31089' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.3.1.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.3.1.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.3.1.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.3.1.min.js\"];\n",
       "  var css_urls = [];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {} // ensure no trailing comma for IE\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"31089\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"31089\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };var element = document.getElementById(\"31089\");\n  if (element == null) {\n    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '31089' but no matching script tag was found. \")\n    return false;\n  }\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.3.1.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.3.1.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.3.1.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.3.1.min.js\"];\n  var css_urls = [];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"31089\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas._libs.tslibs.timestamps.Timestamp'>\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"36d3a603-b9d5-40e3-877c-f47b0c648b0d\" data-root-id=\"31090\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
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\"source\":null,\"start\":null,\"x_end\":{\"value\":1470873600000.0},\"x_start\":{\"value\":1470873600000.0},\"y_end\":{\"value\":19.27},\"y_start\":{\"value\":9.27}},\"id\":\"31660\",\"type\":\"Arrow\"},{\"attributes\":{\"end\":{\"id\":\"31767\",\"type\":\"NormalHead\"},\"line_alpha\":{\"value\":0},\"source\":null,\"start\":null,\"x_end\":{\"value\":1499904000000.0},\"x_start\":{\"value\":1499904000000.0},\"y_end\":{\"value\":22.74},\"y_start\":{\"value\":12.739999999999998}},\"id\":\"31768\",\"type\":\"Arrow\"},{\"attributes\":{\"end\":{\"id\":\"31929\",\"type\":\"NormalHead\"},\"line_alpha\":{\"value\":0},\"source\":null,\"start\":null,\"x_end\":{\"value\":1516147200000.0},\"x_start\":{\"value\":1516147200000.0},\"y_end\":{\"value\":38.27},\"y_start\":{\"value\":28.270000000000003}},\"id\":\"31930\",\"type\":\"Arrow\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.3},\"fill_color\":{\"value\":\"red\"},\"line_width\":{\"value\":0},\"size\":15},\"id\":\"31389\",\"type\":\"NormalHead\"},{\"attributes\":{\"end\":{\"id\":\"31389\",\"type\":\"NormalHead\"},\"line_alpha\":{\"value\":0},\"source\":null,\"start\":null,\"x_end\":{\"value\":1421625600000.0},\"x_start\":{\"value\":1421625600000.0},\"y_end\":{\"value\":10.52},\"y_start\":{\"value\":20.52}},\"id\":\"31390\",\"type\":\"Arrow\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.3},\"fill_color\":{\"value\":\"green\"},\"line_width\":{\"value\":0},\"size\":15},\"id\":\"31173\",\"type\":\"NormalHead\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.3},\"fill_color\":{\"value\":\"green\"},\"line_width\":{\"value\":0},\"size\":15},\"id\":\"31662\",\"type\":\"NormalHead\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.3},\"fill_color\":{\"value\":\"green\"},\"line_width\":{\"value\":0},\"size\":15},\"id\":\"31659\",\"type\":\"NormalHead\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.3},\"fill_color\":{\"value\":\"green\"},\"line_width\":{\"value\":0},\"size\":15},\"id\":\"31767\",\"type\":\"NormalHead\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.3},\"fill_color\":{\"value\":\"green\"},\"line_width\":{\"value\":0},\"size\":15},\"id\":\"31929\",\"type\":\"NormalHead\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.3},\"fill_color\":{\"value\":\"green\"},\"line_width\":{\"value\":0},\"size\":15},\"id\":\"31176\",\"type\":\"NormalHead\"}],\"root_ids\":[\"31090\"]},\"title\":\"Bokeh Application\",\"version\":\"1.3.1\"}};\n",
       "  var render_items = [{\"docid\":\"94567266-264b-4649-ab38-43f8f4c603fd\",\"roots\":{\"31090\":\"36d3a603-b9d5-40e3-877c-f47b0c648b0d\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "31090"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.plotting import figure, show, output_file\n",
    "from math import pi\n",
    "\n",
    "reset_output()\n",
    "output_notebook()\n",
    "\n",
    "df = feed[\"000002.SZ\"]\n",
    "df[\"date\"] = pd.to_datetime(df.index)\n",
    "\n",
    "inc = df.close > df.open\n",
    "dec = df.open > df.close\n",
    "w = 12*60*60*1000 # half day in ms\n",
    "\n",
    "TOOLS = \"pan,wheel_zoom,box_zoom,reset,save\"\n",
    "\n",
    "p = figure(x_axis_type=\"datetime\", tools=TOOLS, plot_width=1000, title = \"MSFT Candlestick\")\n",
    "p.xaxis.major_label_orientation = pi/4\n",
    "p.grid.grid_line_alpha=0.3\n",
    "\n",
    "p.segment(df.date, df.high, df.date, df.low, color=\"black\")\n",
    "p.vbar(df.date[inc], w, df.open[inc], df.close[inc], fill_color=\"#D5E1DD\", line_color=\"black\")\n",
    "p.vbar(df.date[dec], w, df.open[dec], df.close[dec], fill_color=\"#F2583E\", line_color=\"black\")\n",
    "\n",
    "\n",
    "# 绘制持仓股票走势图\n",
    "for idx, code in enumerate([1], start=22):\n",
    "    for order in order_lst:\n",
    "        if len(order[\"deal_lst\"]) == 0:\n",
    "            continue\n",
    "        if order[\"type\"] == \"buy\":\n",
    "            for deal in order[\"deal_lst\"]:\n",
    "                x, y = pd.to_datetime(deal[\"date\"]), deal[\"price\"]\n",
    "                p.add_layout(Arrow(end=NormalHead(fill_color=\"red\", fill_alpha=0.3, line_width=0, size=15), x_end=x, y_end=y, line_alpha=0, x_start=x, y_start=y + 10))\n",
    "\n",
    "\n",
    "        if order[\"type\"] == \"sell\":\n",
    "            for deal in order[\"deal_lst\"]:\n",
    "                p.add_layout(Arrow(end=NormalHead(fill_color=\"green\", fill_alpha=0.3, line_width=0, size=15), x_end=x, y_end=y, line_alpha=0, x_start=x, y_start=y - 10))\n",
    "# output_file(\"candlestick.html\", title=\"candlestick.py example\")\n",
    "\n",
    "print(type(x))\n",
    "show(p)  # open a browser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"24572\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"24572\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };var element = document.getElementById(\"24572\");\n",
       "  if (element == null) {\n",
       "    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '24572' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.3.1.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.3.1.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.3.1.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.3.1.min.js\"];\n",
       "  var css_urls = [];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {} // ensure no trailing comma for IE\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"24572\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"24572\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };var element = document.getElementById(\"24572\");\n  if (element == null) {\n    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '24572' but no matching script tag was found. \")\n    return false;\n  }\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.3.1.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.3.1.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.3.1.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.3.1.min.js\"];\n  var css_urls = [];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"24572\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"46289ffd-0ca1-4d99-9e44-9ec02fe290ab\" data-root-id=\"24573\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = {\"915e48d6-1f7d-4a69-a97e-48a10e6287c5\":{\"roots\":{\"references\":[{\"attributes\":{\"below\":[{\"id\":\"24582\",\"type\":\"LinearAxis\"}],\"center\":[{\"id\":\"24586\",\"type\":\"Grid\"},{\"id\":\"24591\",\"type\":\"Grid\"},{\"id\":\"24611\",\"type\":\"Arrow\"}],\"left\":[{\"id\":\"24587\",\"type\":\"LinearAxis\"}],\"renderers\":[{\"id\":\"24608\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"24614\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"24598\",\"type\":\"Toolbar\"},\"x_range\":{\"id\":\"24574\",\"type\":\"DataRange1d\"},\"x_scale\":{\"id\":\"24578\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"24576\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"24580\",\"type\":\"LinearScale\"}},\"id\":\"24573\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{},\"id\":\"24618\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"text\":\"\"},\"id\":\"24614\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"24588\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"24593\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"ticker\":{\"id\":\"24583\",\"type\":\"BasicTicker\"}},\"id\":\"24586\",\"type\":\"Grid\"},{\"attributes\":{\"formatter\":{\"id\":\"24616\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"24588\",\"type\":\"BasicTicker\"}},\"id\":\"24587\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"24620\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"24580\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"24621\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"radius\":{\"units\":\"data\",\"value\":0.1},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"24607\",\"type\":\"Circle\"},{\"attributes\":{\"dimension\":1,\"ticker\":{\"id\":\"24588\",\"type\":\"BasicTicker\"}},\"id\":\"24591\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"24583\",\"type\":\"BasicTicker\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.3},\"fill_color\":{\"value\":\"green\"},\"line_width\":{\"value\":0},\"size\":100},\"id\":\"24610\",\"type\":\"NormalHead\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"field\":\"fill_color\"},\"line_color\":{\"field\":\"line_color\"},\"radius\":{\"units\":\"data\",\"value\":0.1},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"24606\",\"type\":\"Circle\"},{\"attributes\":{\"overlay\":{\"id\":\"24619\",\"type\":\"BoxAnnotation\"}},\"id\":\"24594\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"callback\":null},\"id\":\"24576\",\"type\":\"DataRange1d\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"24619\",\"type\":\"BoxAnnotation\"},{\"attributes\":{},\"id\":\"24592\",\"type\":\"PanTool\"},{\"attributes\":{\"formatter\":{\"id\":\"24618\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"24583\",\"type\":\"BasicTicker\"}},\"id\":\"24582\",\"type\":\"LinearAxis\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"24592\",\"type\":\"PanTool\"},{\"id\":\"24593\",\"type\":\"WheelZoomTool\"},{\"id\":\"24594\",\"type\":\"BoxZoomTool\"},{\"id\":\"24595\",\"type\":\"SaveTool\"},{\"id\":\"24596\",\"type\":\"ResetTool\"},{\"id\":\"24597\",\"type\":\"HelpTool\"}]},\"id\":\"24598\",\"type\":\"Toolbar\"},{\"attributes\":{},\"id\":\"24597\",\"type\":\"HelpTool\"},{\"attributes\":{\"source\":{\"id\":\"24605\",\"type\":\"ColumnDataSource\"}},\"id\":\"24609\",\"type\":\"CDSView\"},{\"attributes\":{\"data_source\":{\"id\":\"24605\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"24606\",\"type\":\"Circle\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"24607\",\"type\":\"Circle\"},\"selection_glyph\":null,\"view\":{\"id\":\"24609\",\"type\":\"CDSView\"}},\"id\":\"24608\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"end\":{\"id\":\"24610\",\"type\":\"NormalHead\"},\"line_alpha\":{\"value\":0},\"source\":null,\"start\":null,\"x_end\":{\"value\":0.5},\"x_start\":{\"value\":0.5},\"y_end\":{\"value\":0.7},\"y_start\":{\"value\":1}},\"id\":\"24611\",\"type\":\"Arrow\"},{\"attributes\":{},\"id\":\"24578\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"24596\",\"type\":\"ResetTool\"},{\"attributes\":{\"callback\":null},\"id\":\"24574\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"24595\",\"type\":\"SaveTool\"},{\"attributes\":{\"callback\":null,\"data\":{\"fill_color\":[\"navy\",\"yellow\",\"red\"],\"line_color\":[\"navy\",\"yellow\",\"red\"],\"x\":[0,1,0.5],\"y\":[0,0,0.7]},\"selected\":{\"id\":\"24620\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"24621\",\"type\":\"UnionRenderers\"}},\"id\":\"24605\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"24616\",\"type\":\"BasicTickFormatter\"}],\"root_ids\":[\"24573\"]},\"title\":\"Bokeh Application\",\"version\":\"1.3.1\"}};\n",
       "  var render_items = [{\"docid\":\"915e48d6-1f7d-4a69-a97e-48a10e6287c5\",\"roots\":{\"24573\":\"46289ffd-0ca1-4d99-9e44-9ec02fe290ab\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "24573"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.plotting import figure, output_file, show\n",
    "from bokeh.models import Arrow, OpenHead, NormalHead, VeeHead\n",
    "from bokeh.plotting import figure, show, output_notebook, reset_output\n",
    "\n",
    "reset_output()\n",
    "output_notebook()\n",
    "\n",
    "# output_file(\"arrow.html\", title=\"arrow.py example\")\n",
    "\n",
    "p = figure(plot_width=600, plot_height=600)\n",
    "\n",
    "p.circle(x=[0, 1, 0.5], y=[0, 0, 0.7], radius=0.1,\n",
    "         color=[\"navy\", \"yellow\", \"red\"], fill_alpha=0.1)\n",
    "\n",
    "p.add_layout(Arrow(end=NormalHead(fill_color=\"green\", fill_alpha=0.3, line_width=0, size=100), x_end=0.5, y_end=0.7, line_alpha=0, x_start=0.5, y_start=1))\n",
    "\n",
    "show(p)"
   ]
  }
 ],
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